Uncategorized – Gemini Data

Webinar: Boost Supply Chain Resilience with Graph Database and Analytics

Jenn Snider — Wed, 06 Sep 2023 22:17:20 +0000

In the past, supply chain management was focused mostly on cost optimization. The pandemic, geopolitical instabilities, and natural disasters changed all of that.

Today’s supply chain leaders have to focus on both cost optimization and supply chain resilience – the ability to forecast and respond quickly to these disruptions. What’s critical to their success? Digital technologies that enable them to model their entire network, identify potential vulnerabilities, and make informed decisions on how to keep the supply chain moving.

Webinar: Boost Supply Chain Resilience with Graph Database and Analytics

September 21st, 2023

In this webinar, we’ll walk through real-world examples of supply chain resilience in practice from Ennoconn. One of the world’s leading industrial device manufacturers, Ennoconn, uses graph technology from Neo4j and Gemini Data to minimize overstock, control cash flow, and survive disruptions.

During this session, you’ll explore:

How Ennoconn uses graph technology to create digital twins and manage complex networks
The power of graph data science in forecasting and preparing for unexpected events
The role of graph data science in accelerating the digitalization of logistics processes

You’ll also learn how Ennoconn integrated OpenAI’s GPT-X APIs and generative AI to broaden adoption and ensure that everyone can use the application, regardless of their skill level.

No matter your industry, if you’re a supply chain technology leader looking to reduce risk and boost resilience, you’ll gain valuable insights at this webinar.

View the webinar here.

Unlocking New Horizons: 4 Perspectives on Leveraging LLMs and Graph Databases

Jenn Snider — Wed, 12 Jul 2023 20:49:10 +0000

In the vast landscape of information technology, Large Language Models (LLMs) and graph databases have emerged as powerful tools revolutionizing how we process and analyze data. LLMs, such as OpenAI’s GPT-X systems powering the wildly popular ChatGPT, have made significant strides in natural language understanding and generation, while graph databases offer a flexible and efficient way to represent and query complex relationships. When these two technologies are combined, they unlock unprecedented possibilities for knowledge extraction and decision-making. Let’s explore four ways of thinking about LLMs and graph databases, shedding light on their potential applications and synergies.

Natural Language Understanding and Generation

LLMs are designed to comprehend and generate human-like text, making them invaluable for natural language understanding (NLU) and natural language generation (NLG) tasks. By leveraging LLMs in conjunction with graph databases, we can enhance the capabilities of traditional query systems. Graph databases, like Gemini Explore, provide a rich representation of connected data with nodes and edges, capturing relationships and context. This enables LLMs to generate more accurate and context-aware responses by taking into account graph-based context.

For example, imagine a customer support chatbot that utilizes a graph database to store information about customer profiles, products, and common support issues. By integrating an LLM, the chatbot can understand and generate responses in a more conversational manner, drawing insights from the graph structure to provide personalized and contextually relevant solutions.

Knowledge Graph Enrichment

Graph databases excel at representing and connecting heterogeneous data sources. LLMs can be employed to enrich knowledge graphs by extracting structured information from unstructured data. We can create powerful knowledge extraction pipelines by training LLMs on domain-specific corpora and integrating them with graph databases.

Consider a healthcare application that stores patient records in a graph database. By applying LLMs to unstructured clinical notes, the application can extract structured information such as diagnoses, medications, and treatment plans. This enriched knowledge graph can then be leveraged for advanced analytics, medical research, and personalized patient care.

Recommendation Systems

Graph databases provide a natural framework for modeling and querying complex relationships, making them an excellent choice for building recommendation systems. With their ability to understand user preferences and generate relevant suggestions, LLMs can enhance the accuracy and personalization of these recommendation systems.

By combining the power of LLMs and graph databases, we can create recommendation engines that consider both explicit and implicit user preferences. For instance, a movie streaming platform can leverage a graph database to model user interactions, such as watched movies, ratings, and social connections. By utilizing an LLM, the platform can generate personalized movie recommendations based on the user’s viewing history, preferences of similar users, and other relevant contextual information from the graph.

Complex Network Analysis

Graph databases serve as a foundation for complex network analysis, enabling us to uncover patterns and insights from interconnected data. When coupled with LLMs, this analysis becomes even more powerful as LLMs can identify complex patterns in large-scale networks.

For example, social media platforms can employ LLMs and graph databases to detect and understand misinformation spread across their networks. LLMs can identify potentially misleading information by analyzing the text content of posts, comments, and shared articles. The graph structure of the social network can be used to track the propagation of such content and identify influential nodes. This integrated approach helps platforms proactively combat misinformation and protect their user base.

Conclusion

The combination of LLMs and graph databases presents a multitude of opportunities across various domains. The potential applications are vast, from improving natural language understanding and generation to enriching knowledge graphs, enhancing recommendation systems, and enabling complex network analysis. By leveraging the strengths of LLMs and graph databases, organizations can unlock new horizons in data analysis, decision-making, and user engagement.

The synergy between LLMs and graph databases enables a deeper understanding of complex relationships and context. It empowers systems to provide more accurate, personalized, and context-aware responses. Whether it’s in customer support, healthcare, recommendation systems, or combating misinformation, integrating LLMs and graph databases offers a paradigm shift in data processing and analysis.

By embracing these technologies and exploring the four perspectives mentioned above – natural language understanding and generation, knowledge graph enrichment, recommendation systems, and complex network analysis – organizations can unlock new insights, improve decision-making, and deliver enhanced user experiences in today’s data-driven world. The journey toward harnessing the full power of LLMs and graph databases has just begun, and it’s an exciting path to be on.

Ready to get started? Gemini Explore integrates with the latest LLM, GPT, and machine learning advancements for enterprises aiming to leverage the potential of generative AI. We offer a quick, secure, and effective way to integrate LLM technology with your enterprise data to deliver actionable insights and recommendations to drive improved business outcomes.

5 Reasons Graph Data Projects Fail

Jenn Snider — Thu, 08 Jun 2023 17:19:14 +0000

Bringing graph data technology to an organization is not for the faint of heart. You are constantly juggling your budget, schedule, and requirements. Expectations collide with the reality of deploying a graph platform inside an organization. We’ve worked with teams worldwide who are bringing the power of graph data to their organizations, and there are a few roadblocks we see over and over again.

Let’s dive in:

#1. Technical Requirements Don’t Match Business Requirements

When implementing graph technology at your company, avoiding a haphazard approach and adopting a more focused strategy to achieve results faster are crucial. Identify specific areas where graph technology will be applied, the problems it will address, and the metrics it aims to improve. Define the expected outcomes and interactions from the perspective of business users or end-users of the graph data application.

Business requirements represent a software project’s high-level goals and objectives, encompassing the necessary features and capabilities. These are typically expressed in non-technical language and concepts, sometimes ambiguous or subject to change.

Once business requirements have been established and agreed upon, they have to be translated into technical specifications. This detail outlines the actual features and functions the application must have to fulfill the business needs. It’s best to use a structured approach by breaking down business questions into data requirements and mapping them to application features.

Effective communication between subject matter experts on both the business and technical sides is crucial to ensure engineers understand end-user needs and that the targeted end-users provide the most straightforward explanations of what they expect to see when the app goes live.

To address this issue:

Clarify business requirements before starting work: Coordinate with stakeholders, subject matter experts, and future end-users to ensure clear and well-defined requirements.
Involve technical experts early: Ensure experts understand requirements and can provide input on the feasibility and limitations of the technology.
Use templates and documentation extensively: Create use cases, process flows, and data models to map out how the application will meet business and technical requirements.
Validate, test, and re-test: Test technical requirements with prototypes or proofs-of-concept to ensure accuracy and feasibility, involving stakeholders and end-users.
Collaborate and communicate: Schedule regular meetings and updates between stakeholders and the technical team to ensure requirements align with business needs and maintain a common repository for documents and data.

#2: Data Quality Is Low, Data is Hard to Access, and Data Modeling Takes Forever

Data sources and quality are critical to a graph data project’s success. Ensuring that applications can connect to the required data sources and maintain high-quality data is essential.

Preparing data for a graph database differs from traditional relational databases. It’s essential to structure, ingest, and process the data correctly and model it to work with the graph. To do this, you must normalize the data and perform ETL (Extract, Transform, Load) processes compatible with the graph database. Often teams need to learn a specialized query language or use third-party tools to ingest the data effectively. Some Configuration may be required to recognize the entities or nodes and relationships in the data and create the connections between them. High-quality data ingestion and modeling are crucial for success.

Key indicators this problem is rearing its ugly head:

Stalling out to get data right: You are six months into the project, and your team is still struggling with data preparation.
Too much time writing connectors: Your team is overwhelmed with writing connectors and parsers for data sources.
Non-normalized data: Storing redundant data can lead to inconsistency and maintenance difficulties.
Poor connectors: Inadequately designed connectors may result in data inconsistencies or loss, such as mishandling data types.
Inadequate ingestion: An improper ingestion process can cause data quality issues.
Clunky data modeling: Incorrect data modeling can result in inefficient queries, redundant data storage, and performance degradation.

How to fix this issue:

Identify entities: Determine the objects or concepts in your data that will be represented as nodes in the graph database.
Identify relationships: Determine the connections between entities that will be represented as edges in the graph database.
Normalize the data: Break down the data into smaller subsets to eliminate redundancies.
Ensure unique identifiers are unique: Create unique identifiers and properties for each entity and relationship.
Test data connectors regularly: Optimize connectors with better hardware, upgrades, or other fine-tuning.

#3: Learning Curve Discourages End Users

Too often, graph implementations are well underway before stakeholders realize that some graph platforms require end users to learn a whole new set of skills or scripts or a coding language to operate the planned application. Many teams don’t incorporate this potentially long learning curve in their project plans and schedules.

Applications that require a lot of training and are difficult to use will not endear end users. End users are busy and want to get going and find what they want.

How you know you have this problem:

A few highly technical users get a lot out of the app, but everyone else has to beg them (or engineering) for help.
End users balk at the learning curve and abandon the app altogether, and this could even be your more “technical” users who don’t want one more thing they have to learn on top of the pile of technologies, tools, and frameworks they’re trying to stay current on.
No one wants to learn a query language like Cypher or Gremlin. This additional learning curve can discourage end users and slow down the adoption of the application.

How to fix this problem:

Invest in front-end development: Customize the interface to meet the specific needs of the business, making it easier for non-technical users to find, enter, and manipulate data. A well-designed front-end can also provide security features that ensure only authorized users access the database, protecting sensitive data.
Keep what’s under the hood, under the hood: Shield end users from the complexity of the database and application, allowing them to focus on solving their business problems. Simplifying user access to data can reduce errors and increase productivity.

#4: End Users Can’t Share Graphs or Collaborate on Analysis

The most incredible graph application in the world can give an end user a startling analysis that could change the direction of the business. But if that analysis can’t be easily shared with others, that limits its reach. Many graph data packages are currently available as desktop or client-based, single-user applications, which can make collaboration and sharing views with colleagues difficult.

When evaluating a solution or application for graph data projects, whether custom-built or from a third party, examining its support for role-based access and its capacity for creating and sharing knowledge is essential. Ideally, users should be able to capture “snapshots” of their work and share them with others through shareable links, similar to Google Drive’s sharing feature. This functionality aligns with users’ expectations of the applications they use daily.

Another crucial aspect to consider is data enrichment. Allowing users to incorporate additional data or context will enhance the graph, enabling faster and more effective problem-solving, and considering these factors when developing or working on graph data projects will lead to a more collaborative and efficient experience for all users.

How to fix this problem:

Prioritize features that facilitate sharing specific projects and views of data sets with shareable links and snapshots, ideally including view/edit roles. While custom coding may be required, this approach will encourage broader app usage, as graph viewers will be more inclined to use the app frequently.
Consider additional features that allow users to add supplementary data sources to a graph for further enrichment and a more comprehensive view.

Check out how Gemini Explore lets you share snapshots of graphs and add new data sources to enrich.

#5: Analysis Takes Too Much Time

We outlined above how there’s a long learning curve for people to get used to using graph technologies due to specialized query languages and the learning hurdles of learning different processes and experiences to learn to get the most out of a graph data application.

With the recent advancements in generative AI like OpenAI’s ChatGPT and Google’s Bard, graph data has become even more accessible to more people. The hurdle in learning a query language has been dissolved. Previously, users had to learn specific query languages and methodologies to use certain tools effectively. Integrating with generative AI allows users to ask questions using human text input and receive graph data output with context as answers without needing to master the tools.

Graph databases are highly effective in managing intricate and interlinked data, making them particularly well-suited for training generative AI. On the other hand, traditional relational databases primarily rely on tables, rows, and columns and may face difficulties when dealing with intricate relationships. In simpler words, graphs provide a versatile, efficient, and easily understandable structure for organizing information, ideal for training AI models to comprehend complex connections. As a result, graph databases serve as an excellent basis for AI projects.

You can see how Gemini Explore solves this problem with our natural language search that uses human data input and responds with graph data output in our blog post, Generative AI, ChatGPT, and the Future of Graph Technology.

Stay Vigilant, Stay Focused

Introducing graph data technology to your organization is a multifaceted endeavor that requires careful planning, clear communication between the technical and business teams, and a thorough understanding of the organization’s data needs and resources. It’s vital to mitigate the challenges posed by the learning curve for end-users, manage data quality and access effectively, and ensure smooth collaboration and information sharing. Incorporating generative AI can drastically reduce the learning curve and open up the potential of graph data to a broader audience. Implementing a graph database, despite its challenges, can unlock tremendous value and insights, enabling organizations to handle complex, interlinked data efficiently and providing powerful, accessible tools for decision-making. The path may be challenging, but with strategic planning and execution, the rewards can be game-changing.

See firsthand how Gemini Explore lets graph data teams leapfrog over the usual stumbling blocks to get broader adoption, faster time-to-value, and increased ROI.

Upcoming Webinar: 5 Keys to Graph Success

Jenn Snider — Wed, 29 Mar 2023 03:07:56 +0000

If you want to bring graph technology to your organization, be prepared for a challenging journey. You’ll need to balance budget, schedule, and requirements while dealing with the realities of deploying a graph platform.

Save Your Seat Today https://www.geminidata.com/5-keys-webinar-2023/

Join our graph experts for a quick countdown of the five reasons projects fail – and how you and your team can avoid these pitfalls and ensure the success of your project.

You will learn:

The number one time sink of all graph projects – and how to not get trapped!
Where graph projects derail before they’ve even started.
How to skip months of development time in a matter of minutes with existing off-the-shelf solutions.
The perils of the “do-it-yourself” mindset that could add months to your project timeline.
Key strategies for ensuring broad adoption of your graph platform.

Whether you are in the planning phase of bringing graph to your organization or in the middle of implementation, this fast-paced webinar will give you the insights you need to plan for success.

Can’t make the live event? Register anyway, and we’ll send you the recording as soon as we can.

Gemini Data and Neo4j Announce Partnership to Deliver the World’s First No-Code Platform for Graph Data

Jenn Snider — Mon, 23 Jan 2023 14:22:26 +0000

Good morning! All of us at Gemini Data are excited today to announce our new partnership with Neo4j, the world’s leading graph data platform.

Read the full announcement https://hubs.ly/Q01yVCyn0

With Neo4j and our no-code graph data platform Gemini Explore, companies can rapidly expand the use of powerful graph technology throughout the enterprise to quickly discover unseen business opportunities.

Key features:

Seamless Integration: Gemini Explore connects easily to any Neo4j implementation.
No Code: No need to write specialized Cypher scripts or know graph query languages.
Self-Service: Business users can easily connect and turn data into visual context.
Low Cost: No need for an army of developers to work with complex graph databases and queries.
Time-Saving: User-friendly interface accelerates time-to-value for business users.

Gemini Explore enhances and extends Neo4 to make it fully enterprise-ready and available to users across the organization regardless of their technical acumen. This combination gives Neo4j implementations faster time-to-value, a more significant impact on decision-making, and a greater ROI on their graph investment.

Link to press release https://hubs.ly/Q01yVCyn0

Bursting the Bubble of Mortgage Compliance

Jenn Snider — Wed, 02 Mar 2022 18:43:36 +0000

Ka-BOOM?

For the past several years there has been periodic buzz about a possible second housing market bubble exploding. Remember a few years ago when the global economy crashed, because lenders dished out subprime mortgages like snake oil and preyed on the most vulnerable? Very simply, they issued mortgages to people who could not make the payments. Both mortgage originators and mortgage lenders were the culprits.

A component of the crisis not often addressed is the role of investors. Many took advantage of low mortgage finance rates, which played a huge role in fueling the housing bubble. “There’s a false narrative here, which is that most of these loans went to lower-income folks. That’s not true. The investor part of the story is underemphasized, but it’s real,” says Wharton’s Susan Wachter. “Borrowers who got loans for their second and third homes…were not home-owners. These were investors.”

Are we facing another bubble burst? Some say no. “More prudent lending norms, rising interest rates and high house prices have kept demand in check. Regulatory oversight on lending practices is strong, and the non-traditional lenders that were active in the last boom are missing, but much depends on the future of regulation,” according to Wachter.

Regulatory compliance requirements

Dizzying regulations have emerged since the last bubble explosion at state and federal levels, including:

TRID Disclosure audits (TILA-RESPA Integrated Disclosure) which is part of the Dodd-Frank Wall Street Reform and Consumer Protection Act)
Home Ownership and Equity Protection Act (HOEPA)
State Consumer Protection regulations
Real Estate Protection Equity Act Good Faith Estimates (RESPA GFE)
HUD-1 Disclosures
…and nearly countless others

The periodic buzz about a possible bubble burst never seems to get much traction. Complacency is certainly a factor, as the list of regulations continues to grow. You know, that old false sense of security, tacit or explicit? But there are so many factors to consider. Forbes reports, for example, that wages have not kept pace with the rise of housing costs, which means that many renters are now unable to save enough for down payments or mortgage payments. Recent tariffs on Canadian lumber (recently up from 8.99% to 17.5%) have driven up builder costs, and hence inflated home prices and associated lender costs.

Shouldn’t we expect the most devious of compliance loopholes to be uncovered, which could fuel the next bubble burst? Time to wake up!

Consider some of the factors involved in mortgage originator and lender compliance:

Who are the potential borrowers, and what are their socioeconomics?
What is the housing inventory (recently down 23%)?
How has the pandemic affected housing prices?
How does incurred student loan debt affect the ability to obtain a mortgage?
Where are the most affordable and unaffordable housing regions?
How is the Fed manipulating interest rates?
…and the list goes on.

That is a big honkin’ cumulus cloud load of data, no? How do you acquire it, clean it, crunch it, and get your arms around all of it? Using spreadsheets, pie charts, bar graphs, report templates and all that is not only incredibly inefficient, but often leads us to false narratives.

By analogy, ask anyone who has attempted to solve Lewis Carrol’s logic puzzles what they conclude. For example:

Babies are illogical;
Nobody is despised who can manage a crocodile;
Illogical persons are despised.
Therefore…???

How different is that from trying to reconcile two or more of the apparent unrelated challenges listed above? Without tools that reliably support sound logic with real data, you might think that babies despise crocodiles. What’s the story?

What to do…

Choices for mortgage originators and lenders seem to be these:

Don’t you worry ‘bout a thing;
Cobble together as much related data as you can and set an army of spreadsheet geeks to work on predictions that impact compliance. Here are just a few:
- Mortgage interest rates rising to 3.6% in 2022 will bring housing prices down
- New listings will hit a new high without making a dent in supply shortages
- Rents will continue to rise
- Homebuyers will relocate to cities
- Condo demand will rise

The validity of one or more of such “predictions” is intensely complicated. How many data elements are involved? Just look at that list of data sources. How clean and normalized is the data from so many sources? What are the most significant relationships (correlations and causation) between data elements, qua mortgage lending? Do you have the technical infrastructure to crunch the problem and put your house in order? Big questions!

Gemini has shown the way forward for numerous complex problems that involve compliance swimming in huge pools of data. Especially useful are the crystal clear visuals that you can derive using Gemini Explore.

There’s nothing impressive about unscalable analysis techniques on dirty data with less than capable technology. All that does is place the burden of getting it right on a few ultra technical data scientists, with both hands tied behind their backs.

On the other hand, teasing out good candidates for mortgage lending within a reliable, compliant framework—and assessing and reporting, timely and reliably—is what is required for the most significant challenges facing the housing market. Want to learn how to effectively address over 70% of the most nagging risks that mortgage originators and lenders face? Want to put your domain experts to work as analysts, cleaning data, revealing hidden relationships visually, and framing it all within a capable technology footprint? Reach out to our team to start using Gemini Explore today.

Data Contextualization: Cross-Discipline Insights Without the Fuss

Jenn Snider — Thu, 24 Feb 2022 14:45:56 +0000

Big data. You’ve heard all about it since the 1990s. It’s been both the bane of IT’s evolution and the catalyst for so many amazing innovations in digital transformation. Back then, Howard Rheingold wrote forward-thinking books about “mind-expanding technologies.” He was talking about how our intelligence can be amplified with new technologies and, in this context, how powerful cross-discipline collaboration and cooperation can be. Behind it all was the realization that the technology of the day enabled data collection on an unprecedented scale, with the unintended consequence of it outpacing our meager abilities to make sense of it all.

The Virtual Museum of the Frog

An example Rheingold describes in Virtual Reality is the virtual museum of the frog. A properly curated display would not just include a bunch of frogs sitting on a table. It would focus on a distinct species of frog, placed in its natural habitat, while exposing all related flora and fauna. You would get a complete picture of this particular frog, where it lives, how it lives, what it eats, what eats it, how and when it reproduces, and so forth.

The problems that revealed themselves were the stovepiped data repositories that housed, respectively, great photography and video of the frog, another for the biology of the frog, then the ecology of the frog, weather around the frog-a-verse, what the frogs eat, lifecycle of their food supplies, and so many more. For there to be a meaningful virtual museum of the frog, cooperation among the repository stakeholders and their wares is paramount, argued Howard Rheingold.

What he was saying, in part, is that you can’t know the frog unless you know its context. You have to connect the frog data with its biological, ecological, environmental (etc.) data to show all the right relationships visually. After all, the virtual museum of the frog is visual. You see which plants and insects it eats, which foliage it uses for camouflage, how it swims and hides in water, etc. Such high fidelity visuals of those relationships would not be possible without all that contextual data. To be sure, computer horsepower was not (and is not) the limiting factor for a virtual museum. It was cross-discipline cooperation that was elusive. What incentives are/were there to collaborate? Create such incentives, and you might just get everything you need for your virtual museum.

A way forward.

What if you were able to present limited, frog-specific data to someone who could supply all the contextual data at once, make the connections between elements, and present all that visually? Cross-discipline cooperation on steroids! Further imagine that you could cross-reference things like weather and food supplies with mating habits. “Oh,” you might exclaim, “When the fly population explodes in May and the frogs are well fed after the long winter, they begin their mating rituals. AHA! Severe weather in spring that decimates the fly population affects the frog’s reproduction cycle.”

Given a limited slice of data, Gemini’s solutions automatically supply context from hundreds of reliable sources, making tacit relationships explicit, and empowering you to see those relationships visually. It’s as if all the keepers of disparate frog-related data come together to collaborate. Complete disintermediation. You are able to quickly draw meaningful connections and otherwise hidden conclusions. Gemini’s data contextualization exposes related information (context) to make interpretation intuitive. Like the museum of the frog, you begin to see patterns, trends, correlations, and even causation. It tells a compelling story, which is perhaps the realization of Rheigold’s musings.

Evolution of big data.

From the 1990s to the 2020s, big data has been all the news. According to Forbes, Michael Cox and David Ellsworth coined “big data” in their 1997 paper “Application-controlled demand paging for out-of-core visualization” in the proceedings of the IEEE 8th conference on visualization. Many others tried to quantify the amount of data in the world and how it was expanding. That same year, Michael Lesk predicted, “…in a few years, (a) we will be able [to] save everything–no information will have to be thrown out, and (b) the typical piece of information will never be looked at by a human being.”

In the May 2012 article “Critical Questions for Big Data” published in Information, Communications, and Society, big data is defined as “a cultural, technological, and scholarly phenomenon that rests on the interplay of:

Technology: maximizing computation power and algorithmic accuracy to gather, analyze, link, and compare large data sets.
Analysis: drawing on large data sets to identify patterns in order to make economic, social, technical, and legal claims.
Mythology: the widespread belief that large data sets offer a higher form of intelligence and knowledge that can generate insights that were previously impossible, with the aura of truth, objectivity, and accuracy.”

In the following 10 years, technology and analysis techniques have blossomed to become data contextualization and visualization. In a sense, it has turned the mythology of large data sets into reality, but perhaps not the way the “Critical Questions” authors imagined. Gemini Data’s solutions empower organizations to quickly squeeze gems of insight from big (or medium or small) data. All that without the pain of clunky early-adopter offerings. These products are for the majority, totally democratized.

Want to learn more…

… about data contextualization and how it can help your business? Gemini transforms data and analytics by enabling you to intuitively make connections that benefit your business. We can help you effectively transform data into stories that expose, predict, and send you on a successful business journey.

Why Your Business Needs Intelligent Data Pipelines

Jenn Snider — Tue, 22 Feb 2022 13:37:56 +0000

As the volume, variety, and types of data that businesses collect and analyze grows exponentially, so too has the need for an easier way to manage it all. In a world where data is at the heart of most processes within an organization, finding better and quicker ways to collect, clean, aggregate, and align data is critical to ensuring accurate outcomes downstream. In fact, data and data management – if it’s not done effectively – can actually act as impediments to businesses implementing innovations like AI and machine learning that could enhance their operations. Breaking down the barrier of poor data management is where intelligent data pipelines come in.

What Are Data Pipelines?

At their core, data pipelines use one or more software technologies to unify, manage, and visualize the flow of structured business data. This process is usually done strategically, with the goal of improving the overall efficiency of a business. A data pipeline can run in a few different ways, including on a scheduled basis, in an ad hoc or real time manner, or it can be triggered by a set of rules or conditions that are put into place. An effective data pipeline strategy can help businesses both accelerate and automate their processes related to the gathering, changing, cleaning, and moving data to downstream systems and applications.

Additionally, as more businesses implement machine learning and AI technologies, data pipelines will become more intelligent. Adding elements such as logic and algorithms can create an intelligent data pipeline which can support unique requirements for a specific system or application. When feeding your raw data into a decision intelligence platform like Gemini, an intelligent data pipeline is an important tool in more easily collecting and gathering data.

How Can Data Pipelines Help Your Business?

The benefits of data pipelines are varied and can affect a business from sales to marketing to production. Since a data pipeline can automate and accelerate processes such as data acquisition, transformation, and movement, pipelines can boost sales, improve productivity, and accelerate digital transformation initiatives. For example, an intelligent data pipeline in the healthcare industry could analyze the grouping of health care diagnosis-related groups codes – a system that helps better control hospital costs and determine payor reimbursement rates – to ensure consistency and completeness of those submissions as well as detect fraud as data is moved along the pipeline.

Additionally, as more companies adopt automated machine learning and artificial intelligence solutions, many more tasks will be handled by intelligent programs called AutoML. To handle these new, automated tasks, companies will need advanced data enrichment and transformation modules to handle data from various sources like satellite imagery and credit card purchases, as well as neural network and machine learning algorithms to process that data. Intelligent data pipelines can help manage all of those needs.

Conclusion

Many business leaders are being asked to unleash the business value of their data and apply those insights to drive quantifiable financial impact. As data needs evolve and become more complex and challenging, intelligent data pipelines will be a critical tool in keeping up and helping leaders better leverage their data insights. Data pipelines can benefit businesses by accelerating and automating processes, resulting in better decision making, increased revenues, and more.

Gemini was founded with the mission to enable our customers to quickly grasp complex data relationships and effectively mold data into stories. We help businesses solve their biggest data challenges and go from data to insights in no time.

Data Science for (almost) anyone.

Jenn Snider — Thu, 17 Feb 2022 14:54:20 +0000

What’s all this about technology “democratization?” Gartner gets credit for the quotes. It’s been all the buzz for some time, but especially since Top 10 Strategic Technology Trends for 2020: Democratization hit the ‘net. But let’s get something straight. The notion that more people could have access to technology if only it weren’t so darned technological has been with us for centuries.

Kron started it.

After the first person etched an animal on a cave wall with a charcoal stick, envious others would learn how to do it, too. They’d struggle to create a fire and burn a properly seasoned stick until it had just the right amount of carbon to draw figures. The enlightened few could produce a few charcoal sticks and decorate their own cave walls. Then some enterprising person (Kron) who knew how to do it began producing charcoal sticks for the masses. Democratization of technology was born.

See? Now the activity of producing cave art was accessible to anyone who could barter an animal skin or slab of meat for a charcoal stick. Black Stick Kron advertised this new technology with the tagline “You draw on wall nice with Black Stick.” But wait. Not everyone can draw. This sleight of hand made Black Sticks all the rage, with so many cave persons boasting about their artistic skills, no matter how ugly some of those drawings were.

If you had the skill to sketch a deer or a mammoth, you could get right to the task with Black Stick, without ever having to make Black Sticks or even know how to make them! Black Sticks certainly “democratized” cave art, but it really wasn’t for everyone. Sure, there were many more Black Stick cave artists than there had ever been, but not everyone could be or wanted to be a cave wall artist.

Everyone needs it now.

Suddenly it seemed that everyone needed to have the best cave art. It became mandatory not only for the aesthetics, but also for recording life events, predicting when to hunt, when the snows would come, and when to plant. Eventually every household wall was adorned with such things, making life so much easier. You didn’t have to know how to draw or even buy Black Sticks. There was now a large group of generalist Black Stick artists available to enhance the utility and beauty of your cave.

Increased capacity has a great return. The world becomes far less complex when great technology tools are combined with basic skills and know-how to become a commodity.

Fast forward…

It’s been millennia since Black Stick, but today the buzz is about data analytics, machine learning, and all that. Like the elegant calendar etched on the cave wall by a cave art generalist, knowing clearly what mountains of data tell a business is imperative, especially around razor sharp relationships. Today’s Black Sticks are the technology tools with built-in models, frameworks, and techniques to sort the trees from the forest. Democratization means domain experts need not become techno-specialists. They can solve the lion’s share of data analytic challenges by combining their know-how with technology tools that make magic behind the scenes.

Today’s Black Sticks

Computers and storage have become relatively cheap and available. The “cloud” (whatever THAT is) is one of those convenient places to accumulate tons of data, while outpacing your ability to make timely and accurate sense of it all. Until the new Kron over at Gemini created several Black Sticks:

One cleans up, makes sense of, and fosters fast and simple streaming of critical data—while uncovering relevant context—without having to write queries and all that technobabble.
Another displays relationships between data elements visually—another one of Gartner’s strategic trends—giving you simple, usable tools to quickly reveal critical relationships.
Yet another provides an innovative and robust platform that obviates the need for complex hardware specifications, capacity sizing, OS, IP, etc.

Tons of low hanging fruit.

Think about it. You have…

Data assets to protect
Privacy and security to ensure
Data regulations to comply with
Fast-paced competitive strategies to develop and implement
A need to efficiently hire and onboard the best employees
…and the list goes on.

This is the stuff of business success. You have to do it fast and you have to do it right. You can’t hammer and chisel your way through the morass and expect to keep pace with the competition and market dynamics. Imagine if you had to build a clock whenever you wanted to know what time it is. Add clock technology to the work environs by adorning walls with clocks and a casual glance is all you need to know the hour. It’s not about being a clock-building specialist. It’s about having lots of time-telling generalists.

Two ways to crack a complex problem

Raise the skills, knowledge, and abilities of the few who typically solve the problems, right up to the level of complexity. Train them. Give them time to develop technical skills. Fill their heads with what they need to get the job done.

Reduce the complexity of the problem space by introducing simple but capable tech tools that make all that training and skill development unnecessary, especially for everyday subject matter experts. Weave the technology stealthily into the fabric of work and you make its utility ubiquitous.

Which do you choose?

The range of risk and complexity in data analysis problems goes from dirt simple to impossibly complicated. Between those extremes is where 70% or more of the most pressing data science challenges lie. It is within this sweet spot where the few brilliant data science specialists should yield to a small army of generalists with the right tools, freeing the most brilliant to do only the heavy lifting. It’s a win-win, with enormous return on your data analysis investment. Why open a peanut with a sledgehammer when you can get them already shelled AND salted?

Let’s talk!

Today’s data analysis tools from Gemini are all about finding that clear signal in your data’s noise. Visual representations enable you to name that tune in one note. Want to learn more? Learn how Gemini’s solutions can work for you, “democratizing” data science and analytics. Select the link and we’re on it with you. Because business is not a democracy; data science democratization really means competitive advantage and business savvy.

The Pitfalls of Data Outsourcing and How to Mitigate Them

Jenn Snider — Tue, 15 Feb 2022 14:16:39 +0000

What is outsourcing?

Businesses outsource many functions as the most cost-effective and strategic means to maintain focus. A classic example is payroll outsourcing, where relevant employee work data is exported to the payroll vendor, who produces paychecks, direct deposits, records of deduction, and the rest of those things that an internal payroll department would normally do. Other examples can include outsourced customer support, marketing, scheduling, installation, retirement planning, employee assistance programs, and so much more.

More recently, businesses have been compelled to outsource machine learning (ML) as a core component of data analytics, toward continuously improving competitive strategies and position. Similar to classic outsourced functions, data is communicated between the client and the vendor, who returns a bespoke ML model.

Risks of Outsourcing

Each outsourcing example carries risk, as the acquisition and communication of data between different entities invites error–explicitly or tacitly–in a number of categories. Included are privacy and security breaches, communication and process barriers, technology mis-match, dubious remote access protocol, service level lapses, and even geographic location of teams. Further, outsourcing a function requires that a certain amount of process control be yielded to the vendor.

Privacy and Security

Privacy and security breaches are of particular concern, from both ethical and legal perspectives. The EU’s General Data Protection Regulation (GDPR) is a core component of human rights and privacy law; similarly for the California Consumer Privacy Act (CCPA). Guidelines are available around consent management platforms (CMPs) to help organizations comply with such regulations. Under these guidelines user choice and privacy requirements are met through anonymization of personal data that is collected, processed, retained, or destroyed. This is accomplished through a variety of techniques, including generalizing and suppressing, anatomizing and permuting (i.e., de-link relationships between data attributes without modifying them), and more. The goal, then, is to remove the risk associated with legal consequences of non-compliance.

Loss of Control

Further to loss of control, outsourcing anonymization carries risk. For example, beyond a certain threshold of anonymity there is a loss of critical context (especially for ML models), as the proverbial baby is discarded along with the bathwater. At the other extreme, some models are easier and faster to construct when questionable–even illegal–data is included. In such cases legal consequences could be huge. It is therefore important that the outsourced function does not yield too much control to the vendor. The business remains responsible for meeting goals, hence retaining sufficient control of a data-dependent process–through people, technology, and process–is paramount.

Communication and process

Communication and process barriers speak to identifying relevant and legal data for the outsourcing activity, having common understanding (between client and vendor) of business activities that produce the data, and standards for work-product completeness and accuracy. Of critical importance to mitigating risk are sufficiently detailed service level agreements (SLAs), critical success factors (CSFs), key performance indicators (KPIs), and meaningful evaluation protocol between the client and vendor.

Technology mis-match and dubious remote access protocol

Once again, legal consequences can be severe when technology platforms between client and vendor are mismatched and/or if there are leaks in remote access protocol that invite data compromise. If accessing and transferring data is not rock-solid and trusted, both the quality of the outsourced work product and the legal ramifications around data breaches can derail a business initiative, or even an entire business.

Conclusion

Businesses outsource many functions, but the more recent imperatives around ML and data science introduce greater risks than any time before. In simplest terms, the business can delegate authority to the vendor around the tasks necessary to create the work product; e.g., a bespoke ML model. But the business retains responsibility to meet business goals associated with the model. That means minimizing risk associated with outsourcing by ensuring legal compliance around privacy and security, ensuring that measures of quality and success are in place, that technology and technical communications follow rigorous standards of trust, and most of all, that the vendor to which the business outsources critical functions is competent to deliver quality work products.

If your business needs guidance around outsourcing data-intense functions and understanding what the data is telling you, reach out to Gemini Data. We help businesses solve their biggest data challenges and go from data to insights faster .

Why You Need Data Quality Management

Jenn Snider — Thu, 10 Feb 2022 15:02:16 +0000

Enter “digital transformation” into your favorite search engine and you will uncover thousands of contemporary articles on the topic. Important trends for business are detailed, for example, in eWeek’s January 28, 2022 7 Digital Transformation Trends Shaping 2022, including hyperautomation, more effective cybersecurity, further applications of artificial intelligence, speed and agility in decision-making, low-code/no-code tools, and democratization of data and tools. Organizations are investing heavily in machine learning (ML), applying data and algorithms that reflect how humans learn to continuously improve focus and accuracy of business (organizational) decisions.

Critical to the success of digital transformation are data quality and data quality management (DQM). A cogent example of the old adage “garbage in, garbage out” is how ineffective ML (and data analysis, in general) can be if algorithms act on low quality data. Simple examples include inconsistencies in expressing names and addresses. Case studies abound of unsuccessful sales and marketing campaigns that result from poor name and address hygiene. These barely scratch the surface of contemporary data quality issues.

Data metrics and tools have evolved to address data cleansing and profiling around the attributes accuracy, completeness, consistency, integrity, currency (is the data up-to-date?), and relevance to the business problems at hand. In many respects, data cleansing and profiling are analogous to environmental ecology: If you do not clean out the old growth and attend to the new, hundreds of thousands of acres might burn uncontrollably. “Wildfires” downstream of poor data ecology manifest in loss of competitive advantage, decreased market share, misdirected business leads, loss of venture funding, and leaving business opportunities on the table, to name just a few.

Once an optimal set of data attributes are delineated, measures of data quality can be derived and applied to continuous improvement. Consider, for example, the number of errors per unit amount of data, or the rate at which parsing algorithms fail per unit amount of data. Further, such measures can be compared with measures of business outcome quality, making the end-to-end cycle from data acquisition to business decision adaptive—which is, after all, the goal of ML.

Low-code and no-code tools to ensure data quality have emerged, especially in the context of digital transformation. They work hand-in-hand with the tools of data exploration and analysis. While seasoned, highly-technical data scientists may be required to address the most complex data quality issues, these are mostly edge-cases. The majority of data quality issues that organizations face can be addressed by low-code / no-code tools. The best of these tools complement data visualization analysis tools, which are also low-code / no-code.

The benefits of data quality to digital transformation are many, including competitive advantage, razor-sharp sales and marketing focus, rapid system rollouts, and efficiently integrating disparate and emerging technologies. Most importantly, data quality enables organizations to draw a straight line from the investment in data acquisition to its impact on the bottom line.

If your business aims to improve the data that informs business success, contact Gemini. By connecting the dots between data from disparate sources, Gemini helps organizations effectively transform data into stories that provide fast and effective business insights and outcomes.

Can Gemini Explore Make You Better at Wordle?

Jenn Snider — Wed, 02 Feb 2022 14:22:01 +0000

Like the rest of the world, the team at Gemini Data has caught Wordle fever. That got us thinking… could using our product, Gemini Explore, make us better at Wordle?

The thrill of Wordle is experiencing an a-ha moment (guessing the word!) by understanding relationships between data elements (letters) and understanding the context (possible letter combinations). This is exactly what Explore was built for.

Scanning the keyboard is sort of like viewing data in a table; you can eventually come up with the answer, but the visualization is not optimal for how the human brain works. You scan the keyboard mentally plugging in different letters, one by one. For example, last week while trying to solve Puzzle #222, when I got to my second try, my screen looked like this:

And my keyboard looked like this:

Knowing that the word ends in A_K, the scanning process begins. We know that in the English language there are certain letters are never “1^st level neighbors” (e.g. Z+K, or H+K), but we still have to consider them as we go through the keyboard. Once you have uncovered the combinations that will work for that 4^th letter (ASK, ARK, ALK, ACK, and ANK), your brain goes back and says OK, anything ending in ASK, ARK, and ANK are out because I’ve eliminated those letters. Then it’s time to consider ALK and ACK, and think about two-letter combinations that would work up front. STALK? Nope – remember, we’ve already eliminated S. But my brain won’t let go of ST since it’s such a common combination, even though I know it can’t be, but still… STACK? No, of course not. TRACK? No, both T and R have been eliminated. And so on.

Now if we had uploaded a simple CSV file containing all the five letter words in the English language, we could build a model that would return a canvas that looked like this:

With this visualization, I immediately see that there are only two possible answers to this puzzle. There’s still a small element of luck as to choosing the correct answer first, but either way I know I will solve the puzzle in the allotted six steps, and much faster than I would without Explore.

To be clear, we’re not suggesting you use Explore to help you flex your Wordle skills. Besides, there’s no bonus points for solving the daily Wordle fast. At work, however, whether you’re a data scientist, a security analyst, a supply chain manager, or a business user, decreasing time to actionable insights is the name of the game. Gemini Explore was built to do just that.

If your business is looking to break down data barriers and get to real, actionable insights quickly, connect with our graph experts today or check out our Demo Library to see it in action.

What is data exploration and how can it help your business?

Jenn Snider — Tue, 25 Jan 2022 13:43:57 +0000

Analyzing data is one of the most critical aspects of decision making for most organizations, and it touches just about every aspect of a business. Data exploration is the first crucial step in data analysis, where a large data set is explored in an unstructured way to uncover trends, patterns, and outliers. At this stage of data analysis, it’s not meant to reveal every piece of information, but it does help paint an overall picture of what trends and points need to be studied in more detail, which can benefit an organization in many ways.

A deeper dive into data exploration

Data exploration is a strategic process that maximizes the use of a data set and uncovers its hidden insights for fuller interpretation. It focuses on the discovery of data patterns and relationships using methods such as analysis, visualization, and summarization.

Data exploration often involves the following steps:

Identification of a data set: It’s important to Identify your data set before you start any analysis. A few questions to ask include: what data have you gathered and what does it tell about your business? What kind of information are you looking for? What are your challenges? Think about the purpose of your analysis and the questions you’re trying to answer.
Preparation of the data: Get your data ready and assess it. How well is it formatted? How accurate is it? Are there any gaps? Are there any duplicates? Think about how you’d like to see your data presented so you can get an overview of all the important elements you need to paint the bigger picture before delving into the nitty-gritty details down the line.
Identification of patterns: Once you’ve collected and organized your data, it’s time to start looking for patterns to shape and visualize your data in multiple ways. Think about your data from different perspectives and use stats to identify data patterns, relationships, and trends.

The benefits of data exploration

Data exploration is an essential aspect of data analysis that can be applied to multiple areas of a business. It can help uncover previously unknown information and highlight relationships and patterns in data, thus enabling you to gain valuable insights that can help you improve and evolve your business.

In any situation that involves a huge volume of information, data exploration can help cut it down to a more manageable size to help you focus your analysis and maximize your efforts. It can also save you time by guiding you to more useful and actionable insights from the beginning, rather than venturing down multiple paths that could lead nowhere.

Additionally, data exploration can be more than just a tool to resolve problems and make decisions; it can be a way to stay competitive by helping businesses:

Analyze market performance and uncover customers’ insights;
Recommend and create products that better suit a particular customer segment;
Explore business trends and make smarter decisions; and
Identify the best channels to use in marketing and sales;

Conclusion

In a time when gleaning effective data insights can make or break a business, data exploration is a critical tool in getting to fast and useful data analysis. Data exploration can save you time, help keep your business competitive, and make large sets of data more manageable. Whether you’re looking to boost sales or develop new products, data exploration is key in getting you there faster.

If your business needs to understand what its data is saying, reach out to Gemini today. We can help you solve your biggest data challenges and enable you to go from data to insights in no time.

Data Lake or Data Warehouse: Which does your business need?

Jenn Snider — Tue, 11 Jan 2022 14:35:06 +0000

Most businesses collect and store massive amounts of data on a daily basis and are always on the lookout for the best way to store this important information. Data lakes and data warehouses are two of the most common ways to store data and both options support the same goals, but do so in their own way. This may leave you wondering which one is right for your business. Keep reading to understand the differences between a data lake and a data warehouse and which one you should choose to get the most out of your data.

What Is a Data Lake?

A data lake is a large, open repository for all your data. It’s a place where you can store data in any format, without much planning and without too much concern for pre-processing or preparation. The key characteristics of a data lake include:

Data is stored as-is
Data is not standardized or pre-processed
There is no schema or schema evolution
Data has no timestamp
Data is not segmented or aggregated
Data is not processed or cleaned.
Data is stored in any format

Data lakes allow for faster query results using low-cost storage and enable analytics such as machine learning, predictive analytics, data discovery, and profiling. One thing to keep in mind with data lakes is that since data is raw and unstructured, you’ll want to have a strong cataloging procedure in place so that users can more easily find what they’re looking for.

What Is a Data Warehouse?

Unlike a data lake, a data warehouse is organized and structured. It’s a specialized data store that holds metadata and cleans, standardizes, and processes data as it is being stored. The key characteristics of a data warehouse include:

Data is stored in a standardized format
Data is pre-processed, prepared, and cleaned
There is a schema that is enforced during ingestion
There is a data model and metadata
Data is rolled up, segmented, and aggregated
Data has a timestamp
Data is stored in a table structure

Data Lake or Data Warehouse?

Now that you have a better understanding of what differentiates a data lake from a data warehouse, you may still be wondering which one your business should use. But the reality is, you can, and should, use both. In fact, as organizations that use data warehouses see the benefits of data lakes, many of them are evolving their data warehouses to include data lakes to enable diverse query capabilities, data science use-cases, and advanced capabilities.

This means that rather than choosing one over the other, you can store your structured data in a data warehouse and your unstructured data in a data lake. And, you can use a data lake and data warehouse together to accomplish more than one goal. For example, you can use your data warehouse to store data that needs to be rolled up, aggregated, and normalized, and you can analyze it over time. Then, you can use your data lake to analyze untimely data—data that doesn’t need to be rolled up and doesn’t change over time. Ideally, using both a data lake and a data warehouse can help your business get the most out of its data.

The Right Choice

Data is the lifeblood of every organization. With it, you can make smart, informed business decisions. Without it, everything you do will be based on educated guesses or instinct.

Choosing the right data storage solution for your business is key to getting the most out of your data and it’s important that you can store, analyze, and access it easily. Using a combination of a data lake and a data warehouse can enable your business to be agile and flexible in managing all types of data.

If you’re looking to better understand what your data is telling you, reach out to Gemini Data. We can help you solve your biggest data challenges, enabling you to understand and share data stories, and get from data to insights faster.

How Gemini Stream Can Benefit Your Business

Jenn Snider — Thu, 06 Jan 2022 13:22:14 +0000

It’s hard to find a business that isn’t collecting, analyzing, and processing huge amounts of data to inform and influence decision making. Because big data is so critical to how a business operates, it’s equally important to understand how this information is handled and what procedures are being used to manage the lifecycle of data.

This is where data lifecycle management comes into play. Put simply, data lifecycle management refers to a process that helps manage the flow of data from inception to destruction. While there are many interpretations of data lifecycle management depending on the business, we’ll dive into the core principles of the process here.

Creation

Everything starts when data is created or captured. This can come in any form, from a simple image, a PDF file, a document, or even SQL database data. In any organization, a piece of information is created in one of three ways:

Data Acquisition – In this scenario, data already exists somewhere outside of the organization and is only acquired.

Data Entry – Data can also be obtained through manual entry into a system by personnel within the organization.

Data Capture – Capturing data can be done using a variety of tools and devices in a particular process within the organization.

This can be a challenging first step in the life cycle given that information is coming from multiple disparate sources. However, the right platform and tools can make this process much more streamlined. At Gemini, we’ve created one platform that allows you to manage all of your data source nodes in one place, eliminating the need for an army of IT staff and data scientists to help you create your data.

Storage

Once data has been created, you have to find a way to properly store and file it. There are many systems, programs, and software on the market to help with this and it’s just a matter of finding what meets your business’s needs. At this stage, it’s important to ensure that your data is well protected with the appropriate level of security. It’s also recommended to conduct regular data backups and have a recovery process in place in case you need to restore any lost data and to avoid losing any crucial pieces of information that could compromise your customers, clients, or your business.

Usage

Now we’ve come to the fun part of the data lifecycle: using it! Data can be viewed, processed, modified, visualized, and contextualized at this stage. This is the stage where your data starts to work for you and reveal insights that can help with decision making, strategy setting, and reaching goals. At Gemini, we refer to it as “connecting the dots” – the previous stage of creation, along with analysis, allows you to construct a connected view of your business to transform data into stories.

It’s recommended that whatever system you have in place for storing and retrieving data also has an audit trail available for all critical pieces of information. This lets you see who accessed the data, when it was accessed, and how it was used. You can also ensure that all modifications to the data are fully traceable. Depending on the nature of the data, it can also be used by others outside of the organization, such as offshore teams or third-party outsourcing partners.

Archival

Not all pieces of data are needed at all times. That’s why there comes a phase in the data lifecycle where inactive data is moved out of production systems into long-term storage systems. Archived data isn’t mixed with information that’s used in your company’s day-to-day operations. They are stored in an environment where no maintenance or general usage occurs. This keeps your active data and inactive data separate, minimizing confusion or inaccuracies.

Destruction

As more pieces of information are created and captured at an increasing rate, it would be a futile attempt to try and store everything forever, especially if you’re dealing with terabytes of data every minute. Storage cost and compliance issues will hinder you from doing this, which is why destroying data that’s no longer needed is important. However, depending on the industry your business is in, you’ll want to make sure that this phase isn’t carried out until the information has exceeded its required regulatory retention period.

Conclusion

In today’s world, data is king, and understanding the data lifecycle management process is crucial to establishing a systematic way of handling large volumes of data. From creation to storage to usage and beyond, having a clearly defined and documented data lifecycle management process will ensure your business is effectively (and efficiently) handling its data.

Gemini Data is your partner when it comes to solving the biggest data challenges you may face in your organization. When you work with us, you can get instant business context in one platform – making it easy and streamlined to leverage the power of your data. Contact us today to learn more about how you can from data to insights in no time.

What to Look for When Choosing a Data Source

Jenn Snider — Thu, 23 Dec 2021 15:20:02 +0000

While decision-makers at many businesses have realized the power and potential of external data, many are still struggling to figure out how to source their data. As businesses use more and more data to inform decision-making, they’re increasingly leveraging a new kind of specialist – a data hunter who can seek out and identify valid sources of external data. However, building that type of capability is challenging and it can be hard to keep pace as data source lists grow. Additionally, finding good data sources in-house can be a time-consuming and costly endeavor for a business to take on. As a result, many organizations are looking to outsource their data sources. In fact, according to a Forrester report, 66% of decision-makers surveyed said they’re using or planning to use external service providers for data, analytics, and insights.

So how can a business be sure that they’re using quality sources for their external data? Keep reading as we dive into a few of the characteristics to look for when sourcing external data.

High-Quality

One of the first things to look for in an external data source is the quality of the data as this will directly affect multiple outcomes across your business, from informing decision making to influencing marketing strategies. Businesses will want to make sure that the data they’re collecting has comprehensive and high-value text descriptions to make processes like machine learning and natural language processing easier and more accurate. Consider working with a vendor who can help you confirm your data source selections with industry subject matter experts and identify any missing data types or parameters.

Continuously updated

No matter how reliable a data source is, it’s vital that the data is updated regularly, and most importantly, that your business is able to access those updates. Most data sources are regularly updated as parameters, data types, business processes, and more, change. For example, any data pertaining to customer behaviors for a specific type of product are going to be subject to frequent changes as buying patterns fluctuate. Leveraging a platform like Gemini can not only help your business make sense of disparate data sources and their changes, we can also help you move to the next – and more complicated – part of data analysis: connecting the dots and contextualizing the data.

Relevant information

It may seem obvious, but it’s critical that the data source you use is relevant to your business and industry. Gathering and using data that is not germane to your business could throw off your data analysis and lead you down the wrong path. For instance, collecting too much data from a niche brand could introduce bias into your data sets. When reviewing a data source, you want to make sure you’re gathering information from across a spread of markets where there is relevant data. Comprehensive data sources such as Amazon or Walmart often hold vast amounts of data across a range of industries that could be relevant for your business.

Strong online presence

It’s important to make sure that your data sources have a robust presence online to help you make connections among your data. For example, product reviews and online discussions can be critical to making connections between a product and what customers think about it. Sourcing reviews and online forums can provide you with product details, customer sentiment, publication dates, and more, to help you identify exactly how consumers view a product.

Conclusion

The use of external data in decision-making, improving business processes, and more, can help your company improve its performance and achieve its goals, especially if the data is high in quality, updated regularly, relevant to your business, and has a strong online presence. By looking for these characteristics in a data source, you can ensure that you are using the best possible data to get more reliable and effective decision-making results.

At Gemini Data, we’ve simplified the data analysis process, from gathering data from different sources to contextualizing insights to tell a compelling story, Gemini can be your partner in connecting the dots. Schedule a call or start your free trial of Gemini Explore today! to see how we can help your business get from data to insights faster.

How to Make Invisible Analytics Seamless

Jenn Snider — Mon, 20 Dec 2021 16:20:31 +0000

It’s no secret that data analytics is being used in just about every aspect of modern life and that businesses are using it to inform and shape smarter decision making. Despite this, many organizations have found themselves lagging on using analytics to their fullest, with some reporting that their in-house analytics adoption has stalled around 30%. So how can businesses get decision intelligence into the hands of decision makers and truly leverage analytics to stay competitive? It may seem counterintuitive but the solution could just be to make analytics invisible. Intrigued? Keep reading to learn more about how invisible analytics could increase your business’ use of data.

Leverage consumer-style analytics

We may not always be aware of it but we’re swimming in analytics almost every day. Music services like Spotify and Pandora are constantly using algorithms to analyze listeners’ preferences and provide suggestions for new artists, albums, and tracks to enjoy. Navigation systems like Google Maps and Waze analyze traffic patterns and user behavior to suggest shorter routes and warn of accidents or other hazards. These are all examples of invisible analytics experiences, and they’re so seamless that we don’t even realize we’re relying heavily on analytics for day-to-day decisions. This is how businesses should serve up decision intelligence – deliver actionable insights from data seamlessly.

Empower other applications

As you’ve been reading this article, how many alerts, notifications, buzzes, and pings have gone off on your screen? Just think about all of the apps and software your business is using. From Slack to Teams to Asana, people are bombarded by notifications and to-do’s all day long. You certainly don’t want to add another program in the mix to fight for attention (that will probably get ignored). Instead, build in actionable insights on the platforms teams are already using. Serve up the right piece of decision intelligence to the right person, at the right time and place, guiding them to the best next action. Don’t make anyone open up another app or go hunting for what they need next. Companies would be best served to complement their workforces with actionable decision intelligence and choose analytics platforms that allow for simple, personalized, custom analytics inside any program you’re already using (hint, Gemini has all of that).

Get technology out of the way

No one piece of software or technology is a silver bullet to analytics adoption. If it was, we wouldn’t be talking about this. But by infusing workflows and apps with actionable intelligence, teams can focus on the right outcomes instead of finding the right technology. By putting a purpose-driven analytics platform in place, your teams will be able to infuse advanced insights from complex data sources, with AI-driven capabilities, into workflows they’re already using.

Conclusion

The next wave of data analytics is already here, and it’s imperative that businesses leverage their power to stay competitive. The business world needs to approach in-house analytics adoption the way consumer software companies view customers. Teams need seamless actionable insights in the tools and platforms they’re already using and analytics experiences that mirror consumer ones.

If you’re wondering how your business can improve its decision intelligence and produce seamless data insights, reach out to Gemini today. Our full stack system can help you solve your biggest data challenges and get to insights faster.

How Gemini Explore Works

Jenn Snider — Thu, 16 Dec 2021 13:07:17 +0000

At Gemini Data, we say, “People only hear statistics. But they feel stories.” What we mean is that data on its own is powerful, but with a good story, it’s unforgettable. And that’s what Gemini Explore does – it transforms data and analytics by enabling users to easily and intuitively interact with data using contextual storytelling. It’s all about simplifying and making it easier to see, understand, and communicate the complex – so people can learn faster and do their jobs better.

So how do we do it? There are many features of Explore that make it powerful and easy to use. We’ll dive into just a few of them here.

Simple, accessible data onboarding

If you’re using a graph database to do knowledge graph exploration, the standard process for any data engineer or scientist is to physically onboard the data, a process where the required knowledge and technical skill set for most databases is limited to a certain group of people – the data engineers and scientists. Once that data is onboarded, the challenge is ensuring that you have both the right information and enough information without having too much.

But Explore dramatically reduces the complexity of the onboarding process and consolidates it within a single interface where a user can just pop in the data, go through the wizards, create the relationships for the object they need, and produce a graph they can explore.

Conditional Displays

This is a feature we’ve built in that based on the value of a particular set of notes that a user selects, they can choose the size or the coloration of the note. One Explore use case we like is movie databases (you can check it out in our Demo Library). For example, if a movie makes more money over a certain time period, you can set the size of the note to be different – so the lesser amount would be smaller and the larger amount would be bigger. Additionally, this also applies to the edges or the relationship between the notes. A user can adjust the coloration and the thickness of the line to better connect two dots.

Users can also set two separate conditions. For example, you can set sci-fi movies to green and dramas to blue, and so on, allowing users to more clearly distinguish relationships within larger data sets. The user gets to choose the condition they want to match it against.

Unmatched Data Exporting

Once you’ve onboarded your data, you can begin to explore the data on the graph. From there, a user can export the data however they need. Continuing with the movie database example, if a user has an entire movie database ingested into Explore, but they only want the data set that matches a certain genre, they can do that within the platform. A user would choose another condition, let’s say a particular director with gross sales over a certain number, and they can export that using any decision intelligence platform to create a line chart, pie chart, etc. inside that tool. We’re currently working to connect Explore directly with decision intelligence tools and make it possible to suggest the type of visualization tool a person should use.

Powerful Data Model

Explore’s data model is one-of-a-kind and completely no-code. With our process, all you have to know is how to click. That’s it. No data science degree or coding experience required. In fact, no-code or low-code is the focus of Explore (and our whole suite of products) so that anyone who wants to do the job, can do it. We’re aiming to achieve data nirvana, where you have a set of data that you feed in, and you just get the result that you want to see. No need for going through different programming, acquiring a new skill, or learning to write code. Just the results you need simply and quickly.

Better Data Contextualization is Possible with Explore

At the end of the day, we want to enable our users to bring the contextual data analysis and commentary they need to fully appreciate an insight, and do it in one platform without an army of data scientists. Explore can uncover connections using graph techniques in combinations of diverse data at scale; help organizations accelerate capabilities to anticipate, shift, and respond; find relationships between people, places, and things; capture knowledge to make it easier to perform queries and answer questions, and increase understanding around organizing and preparing data.

If you’d like to learn more about how Explore works, check out our demo library across a range of industries, or schedule time to talk with one of our data experts.

How to Leverage Ad Hoc Data Reporting

Jenn Snider — Tue, 14 Dec 2021 12:48:33 +0000

The growing amount of data that is collected and organized in an organization means that leaders and professionals can make better decisions, but it also means they have to trust the data insights they receive. This is only possible if data is accessible, accurate, and up-to-date. Ad hoc reporting and analysis tools can provide organizations with precisely what they need, when they need it, empowering decision makers to answer critical questions in real-time.

So what exactly is ad hoc reporting and how can your business leverage it? Keep reading to find out.

What is Ad Hoc Data Reporting?

Ad hoc reporting is data analyses or reports that are curated and created by users, as and when they need it. It’s a one-time approach to reporting and analysis and is typically initiated based on new information or a need to identify data patterns. Ad hoc reporting in decision intelligence is in complete contrast with the managed reports seen in the early days of business analytics, which relied on templates distributed by IT departments. But today, data in business is constantly changing, with new data being created and others becoming obsolete. Ad hoc data reporting tools enable users to create a new report in real-time with just a few clicks. This type of reporting is also called “developer-driven” or “self-service” reporting.

Why Use Ad Hoc Reporting?

With ad hoc reporting, a user can easily pull together any dataset to answer a specific and real-time question. With the right platform, such as the full stack Gemini system, ad hoc reporting removes the time and cost of incorporating new data into insights, allowing far more freedom in asking the critical questions, without the effort trap of relying on existing analysis.

While ad hoc reporting is helpful for responding to specific questions and needs, it also enables users to create insights and patterns from the data. It allows for the identification of trends and patterns of behavior, giving you a better understanding of how things work and where you can make improvements.

How to Generate the Reports

To use ad hoc reporting in your business, start by considering what information or data you need or what questions need answering. Gemini Data can be a reliable tool to create, execute, and deliver ad hoc reports. You can simplify data contextualization, analysis, and report management, and focus on driving insights and creating actionable business value. You can also view your reports in real-time or schedule reports to run at a specific time.

Through the Gemini Data platform, you can customize each ad hoc report to be what you need and easily view and update data rather than starting from scratch.

The Future of Ad Hoc Reporting

The right analytics and decision intelligence platform can help you infuse actionable intelligence, resulting in faster results and deeper discoveries. Daily tasks can also be incorporated into workflows, nontechnical teams can be empowered to answer new questions as they come up, and data teams can go deep to bring back groundbreaking insights. As business data grows, ad hoc reporting will be even more critical to keeping up with it and accessing the correct data quickly.

Conclusion

Ad hoc reporting and analysis are valuable tools that provide clear, easy-to-read insights that can transform your business and give you the power to make real-time decisions while also providing you with the ability to access information when you need it.

If your business is looking to leverage the power of ad hoc reporting with the right decision intelligence platform, reach out to Gemini. Business users and data scientists alike can leverage all of Gemini’s products – Explore, Central, and Stream – to easily transform and intuitively interact with their data using contextual storytelling. With Gemini’s system, businesses can derive insightful information from data enabling them to accelerate their decision making process and increase analytics team efficiency, as well as capture new opportunities.

How Big Data Analytics Can Benefit Businesses

Jenn Snider — Mon, 06 Dec 2021 21:49:43 +0000

Big data analytics solutions, which use specialized software tools and applications for predictive analytics, data mining, forecasting, and optimization, have become increasingly important for most businesses, no matter what industry they’re in. With these tools, organizations can gather vast amounts of organized and unstructured data, sort and analyze it, and extract patterns and essential business insights. This data has emerged as a significant differentiator in assisting businesses in forecasting and making strategic decisions to stay competitive, boost revenue, mitigate risk, and achieve growth.

Keep reading to learn more about how big data analytics can help your business.

E-commerce & Retail

Many retailers have adopted a business-driven and pragmatic approach to big data. In fact, sixty-two percent of retailers report that the use of information (including big data in retail) and analytics is creating a competitive advantage for their organizations. Some of the most effective strategies for leveraging big data include identifying specific business requirements first and then customizing the infrastructure, such as types of analytics tools, to support those requirements.

In practically every e-commerce and retail selling/buying process stage, big data analytics plays a critical role. It can help predict trends, identify new consumers, optimize pricing models, segment customers based on purchasing behavior, and present tailored, real-time offers based on client preferences. For example, let’s say a retail outlet’s most valued customers “liked” the Food Network on social media and have shopped frequently at Whole Foods. From there, the retailer can then use these insights to target their ads on social media channels for cooking-related shows and organic grocery stores. This will likely result in higher conversion rates and lower costs for customer acquisition strategies.

Manufacturing

The lifeline of the manufacturing industry relies on hundreds – if not thousands – of daily interactions with mechanical equipment, electrical relays, sensors, and more, all of which are coordinated and controlled by complicated systems. To keep things running smoothly, thousands of metrics and signals must be monitored at all times. Many businesses in the manufacturing industry have shifted their attention from traditional monitoring procedures to a more flexible and real-time process using big data analytics tools. This allows them to create tactical insights to significantly boost corporate performance while addressing emergent issues.

There are many use cases for big data analytics in the manufacturing industry, including:

Predictive Quality: Similar to predictive maintenance, this allows manufacturers to track the many variables that can affect product quality and help determine root causes and factors that contributed to lower quality products.
Supply Chain Management: In manufacturing, timing is everything. Big data helps better predict if and when a supplier will deliver and makes it possible to reduce risk by optimizing supply chains.
Improving Throughput and Yield: There are various factors that can negatively (or positively) impact product yield, and by utilizing the insights from big data, manufacturers can identify patterns in various processes to predict product yield and proactively make improvements.

Healthcare

Because the healthcare sector regularly generates large amounts of data (think about the sheer volume of data that flows through insurance companies), big data analytics plays a critical role in keeping this industry moving. In fact, healthcare is one of the most promising sectors for big data utilization. Moreover, it also helps address some of the industry’s most pressing issues, such as patient profiles, genomic analysis, public health monitoring, fraud analysis, and more.

One timely example where big data can help healthcare companies is the prediction of mass outbreaks. Using big data, scientists and doctors can build models of population health and create predictive models of how an outbreak might progress in a certain population. This can help in multiple ways including the development of vaccines, preventing hospital overcrowding, and issuing quarantine recommendations.

Travel

The travel industry generates a large amount of data in the form of bookings, queries, itineraries, fare charts, and consumer feedback, which leads to extensive data trails. The travel sector can provide a significantly better client experience and boost business efficiency by harnessing all this information using big data analytics. Strategic marketing, improved customer experiences, and reputation management can all benefit from the power of big data analytics.

An example of big data analytics in the travel industry is United Airlines’ use of customer data. They analyze over 150 variables in each customer profile to measure everything from past purchases to customer preferences. As a result, they’re able to create compelling and tailor-made offers for their customers. Big data techniques have increased United’s travel industry year-to-year revenue by over 15%.

Another example is improving the customer experience. One of the biggest travel headaches is lost or misplaced luggage. Through Delta Airlines’ app, customers can track their bags using the same technology as the Delta ground staff. Approximately 11 million Delta customers have downloaded the app globally.

Conclusion

The benefits of big data analytics across multiple industries is countless. From healthcare to travel to manufacturing, big data allows businesses to better target their marketing strategies, streamline business processes, and improve the customer experience, just to name a few. With the continued innovation of data management and analysis tools, businesses will be able to continuously improve upon their use of big data analytics to make intelligent choices that help them improve revenue, save expenses, and boost growth.

If you’re wondering what your big data is telling you, reach out to Gemini Data today. Our full stack system simplifies data management and analysis, allowing your business to connect the dots between data from disparate sources and effectively transform data into stories.

How to Build a Business Case through Data Storytelling

Jenn Snider — Tue, 30 Nov 2021 15:17:28 +0000

In a highly competitive and digitized world, data has become a critical source of business information on many fronts – from decision making to improving processes. But understanding what data is saying and deriving useful insights from it can be a taxing endeavor, and it’s critical to leverage the power of data in such a way that decision makers can use it effectively. That’s where data storytelling comes in. If you want your business to succeed and to be the preferred brand for your customers, understanding how data storytelling can inform and influence decisions is key.

Keep reading to learn more about how you can use storytelling to build your own business case for a new idea.

Establish the Context

The first step to a successful business case is sharing the full context of the situation and what you’re trying to solve for. Using data, narratives, and visuals can help decision makers clearly understand what is happening to the customer, their pain points, and how you can solve them. Developing a story around what customers are experiencing (good or bad) builds empathy between you and your audience, making it easier to persuade them to go with your idea. It also helps stakeholders actually experience the pain or challenges that customers are going through.

Explore options

Most businesses collect vast amounts of information about their customers – from their location to buying habits. This data is critical to understanding what customers want (or don’t want) and how your business can meet their needs. You can get direct knowledge from your audience and infuse your business ideas with that information to help explore how you can better serve customers. For example, if you know that your customers want faster resolution to problems, you can explore all the options available to achieve that. This offers a different perspective on the problem and gives you insights that you may not have discovered otherwise. Additionally, including a story around a specific data point (a.k.a. a challenge) will make your idea that much more appealing to decision makers.

Get Good Data

By utilizing all the data your company is collecting, you can build the meat and bones of your proposal. Use analytics, marketing reports, research, and customer surveys to back up all your claims. If you find anything that sounds good on your pitch but you can’t back it up with the data to make it rock-solid, then you probably need to look for other options. Consider using data visualization to present the data and make it easy to digest. No one wants to read tables upon tables and rows of figures that don’t make a lot of sense. You need to paint a clear picture of what you’re proposing by using the data that you have and creating a compelling narrative.

Pitch Your Idea

Now that you have a clear idea of your business case (with solid data to back it up) you can start pitching. The best way to approach this is to explain it in a balanced manner by sharing both the challenges and opportunities your pitch may encounter. Then you bring out the big guns – your data – to project how your new concept solves the customers’ problems. And not only do you use your data to back up your idea, you share a compelling story that clearly demonstrates how your idea will benefit customers.

Emphasize the Business Value

You already have the idea, the context, varying perspectives, and the data. Now, what you need is to justify why the idea is good. Ask yourself the following questions:

What meaningful impact will it have on the business?
What will it take to solve the problem?
How can I measure its success in terms of business outcomes?

These details are crucial to supporting the emotional aspects you raised in the data story. You want to clearly demonstrate – in hard, cold facts – that you have thought about how to put these data insights into action.

Conclusion

A good idea can be easy to come up with, but when it comes to actually making a case for how it can improve an organization, it can get exponentially harder to make it a reality. Thankfully, data storytelling can help. You can establish context, explore options, and make a solid business case using the power of data storytelling. It’s hard to argue with data, and with emotion and compelling stories, you can make your business case even more powerful.

If your organization is looking to leverage the power of data storytelling, contact Gemini Data today. We help organizations construct a view of their business by connecting the dots to tell their story. It’s our goal to enable customers to quickly grasp complex data relationships and increase the pace of human knowledge and advancement with data storytelling.

Breaking Down the Pain Points of Big Data Analysis

Jenn Snider — Wed, 17 Nov 2021 20:38:32 +0000

There’s no debating the importance of data for businesses. From understanding customer behavior to onboarding new employees to creating effective marketing strategies, data can influence and inform virtually every aspect of an organization. But, the downside to collecting and using data is the sheer amount of it which can put a strain on a company’s data management processes. An organization may find itself struggling to keep up with processing power, storage capacities, and network performance as its data grows exponentially.

Fortunately, there are solutions to address the pain points of big data analysis, which we’ll dive into later in this article. But first, let’s take a closer look at the challenges that arise when collecting and managing big data.

Disintegrated data sources

Handling an influx of massive data coming from different sources can be overwhelming, but unfortunately, this is the nature of collecting data across an organization. Trying to sort and filter out the right data could take weeks or months (or longer) and require an army of data scientists and engineers. When a business needs to make real-time decisions or proactively address a challenge, a slowdown could result in missed opportunities, disgruntled customers, or decreased sales.

Ineffective data classification

Before you can make sense of your data, such as identifying patterns or trends, you have to get all of it into one place to analyze it. Without the right tools, this can mean manually cobbling together data from different places into a spreadsheet or similar platform. Not only is this time consuming but it’s ripe for errors. Any time a task is manually handled by a human, there is a chance (or even likelihood) that mistakes will be made. And if data is being pieced together from multiple sources, the chances of inaccuracies and errors increases. This could mean making decisions or setting strategies based on inaccurate data.

Lack of context

Another critical part of analyzing data is understanding its context. Data without context is open to different misinterpretations, misunderstandings, and reaching the wrong conclusions. By definition, “contextualization” means adding related information to something in order to make it more useful. When applied to data, this can mean understanding the timeframe or previous benchmarks for a particular data set. This context can make finding correlations, patterns, and trends easier.

Complicated reporting

To make data actionable, it’s critical to be able to present it to decision makers in a clear and precise way. Without that, you’re just presenting a random smattering of numbers, graphs, and charts. Many organizations use tools that require cumbersome and overly complicated reporting for each data insight that doesn’t make actionable decision making easy. Precious time and resources could be wasted producing reports that aren’t useful, don’t tell a clear story, and take too much time to create.

The solution: one single platform

Now that we have a better understanding of the challenges that can arise during big data analysis, let’s look at some solutions – actually, one solution.

At Gemini Data, we’ve created a solution that addresses and solves all of these problems in one place. By connecting the dots between data from disparate sources, our tools help organizations effectively transform data into stories. Rather than struggling to organize disintegrated data sources, pulling data together into one spreadsheet, and creating ineffective reports, Gemini Explore allows businesses to seamlessly integrate multiple data sources, quickly manage and correlate data, and tell better data stories.

Gemini Explore transforms data analytics by enabling anyone to easily and intuitively interact with data using contextual storytelling. It’s all about simplifying and making it easier to see, understand, and communicate the complex – so people can learn faster and do their jobs better.

Not only does Gemini help mitigate some of the biggest big data analysis challenges, it has multiple use cases across many industries. From insurance fraud detection to cybersecurity alert triage to investment prospecting, Gemini can help your business connect the dots and get to insights faster.

If you’re experiencing challenges in your big data analytics, start your free 14-day trial of Gemini Explore today to see how we can help your business.

The Power of Graph Technology

Jenn Snider — Thu, 11 Nov 2021 16:51:48 +0000

As organizations continue to manage more and more data, many of them are increasingly turning to graph technology to leverage data for decision making. In fact, analysts predict that by 2025, 80 percent of knowledge and analytics improvements will be made using graph expertise. That number is currently at ten percent which means that we’ll be seeing an explosion in the use of graph technology over the next four years.

So why is graph technology so critical? While traditional relational databases can only connect with one other data point at any time, graph databases connect multiple data points for quicker, easier, and more complex data exploration. Additionally, graph databases can handle vast amounts of data that are overwhelming older, more traditional tools. But these facts are just the tip of the iceberg of the power of graph technology.

The Core of Graph Expertise

As we stated earlier, at the core of graph expertise is relationships between data points. The advantage of saving data points in graph databases is that they can connect with different information factors at any given time, allowing for deeper, faster, and more definitive data analysis and exploration. That can lead to quicker and more robust insights that drive and influence key decision making.

Moreover, analysts say that the best way to break down more traditional and inflexible architectures and instruments is to utilize graph expertise and optimize its effectiveness in combination with augmented intelligence and the cloud.

Graph Usage in the Real World

When it comes to the capabilities of graph expertise, there are many real-world examples we can turn to. One timely example deals with the significance and unpredictability of supply chain administration during the COVID-19 pandemic.

The supply and demand of merchandise has been in constant change during the last 18+ months of the pandemic, meaning that producers have had to react quickly to these changes without having too much or too little product supplies in one region. Additionally, municipalities have needed to provide and ship meals to residents who couldn’t leave their homes, while cities had to figure out the best routes to optimize supply pace and transportation assets.

Another example is how environmentalists are tracking the movements of penguins to understand the impact of climate change on their migratory and mating patterns, allowing them to plan intervention methods for the penguins.

While these two examples may seem completely unrelated, they both involve different combinations of videos, audio, textual content, and transactions, all of which require exploring and understanding complicated data relationships to get to insights. From production supplies during the pandemic to climate change, graph expertise and artificial intelligence have been used to solve urgent problems. Given the urgency of the pandemic and climate change, making connections between data points and quick data-driven decision making has never been more important, and graph technology has risen to the demand.

The Future of Graph Technology

Gartner predicts that by 2023 graph technology will play a role in the decision-making process for 30% of organizations worldwide. In fact, many businesses are already using it in a variety of ways. Decision making has to be even quicker and made in ever-changing and complex environments, and graph technology will help enable this shift. From allowing for a better understanding of customers to detecting fraud to optimizing crop yields, graph technology has powerful applications across a variety of industries.

Data and analytics are more critical than ever before, and the use cases for graph technology will only explode as businesses need ways to drive decision making and stay competitive.

Conclusion

With more and more businesses relying on data to set strategies and inform decision making, there is a need to leverage tools that can handle the increasing volumes of data they’re managing. Graph technology lets users connect multiple data points at the same time, leading to quicker and deeper insights. Additionally, the use cases for graph technology – from agriculture to healthcare – will only continue to skyrocket as businesses look for more ways to manage their data.

Gemini helps organizations construct a connected view of their business without the hassle of graph database development and integration. Our universal no-code data ingestion engine, wizard guided data mapping, plus highly intuitive user interface are all available in one single SaaS platform to facilitate your rapid data contextualization needs. Now your graph technologies projects can be accelerated and achieved quickly and easily. If you’re ready to take the next step with the no-code graph database technology anyone can use, schedule your demo today.

What are the stages in data lifecycle management?

Jenn Snider — Tue, 09 Nov 2021 16:54:15 +0000

Creation

Data Acquisition – In this scenario, data already exists somewhere outside of the organization and is only acquired.

Data Entry – Data can also be obtained through manual entry into a system by personnel within the organization.

Data Capture – Capturing data can be done using a variety of tools and devices in a particular process within the organization.

Storage

Usage

Archival

Destruction

Conclusion

The Ins and Outs of Data Contextualization

Johnny Lin — Tue, 02 Nov 2021 05:12:00 +0000

In a world overrun by 24/7 information, it’s easy to get overwhelmed or misunderstand insights we glean. This holds true for businesses too, which must be sure they are making evidence-based decisions, developing the right strategies, and hitting goals.

While data can be an invaluable resource for businesses looking to make better and smarter choices, it can be complex, too. That’s why one of the most important concepts for businesses to understand and use is data contextualization. With the help of data contextualization, you can analyze big data easily to identify patterns, trends, correlations, and gain valuable insights from the data you’ve collected.

If your business is looking to better understand what your data is telling you, keep reading to learn more about data contextualization and how it can get you from data to insights in no time.

What is contextualization?

According to the proceedings of the 50th Hawaii International Conference on System Sciences, context is information about a certain entity that can be used to reduce the amount of reasoning required for decision-making that’s within the scope of a specific application.

When applied to data analysis, contextualization helps to identify relevant information that can help determine patterns, trends, and correlations. With this data integration, you can provide context to users allowing for better interpretation of your data and enabling you to make smarter decisions.

What is big data?

Big data is a field that treats ways to analyze, systematically extract information from, or otherwise deal with data sets that are too large or complex to be dealt with by traditional data-processing application software.

Though data with many categories or columns offer huge statistical power, it could still lead to a high false discovery rate. With that, big data challenges may include data storage, capturing data, search, sharing, querying, transfer, data source, and more. Seeing as it’s a complex concept, working with big data analytics to reduce the obstacles in obtaining data is vital.

The power of data contextualization

Adding context to data means including background information, patterns, trends, outliers, and more, to help a reader make sense of what the data is really saying. For example, in the retail industry, a reported drop in sales during a given month is not valuable without considering things like traffic patterns, previous benchmarks, holidays, and more. Once you have all of that information, a story begins to emerge. It could be that the drop in sales happened over a holiday weekend when most customers go out of town, and isn’t anything to worry about. Or it could be a troubling trend that requires attention.

More generally, when data is properly contextualized, businesses can use it to guide customer relationships, improve marketing strategies, predict future economic trends, and manage risk.

From there, you can employ data storytelling to create a story from the data analysis you’ve obtained. This allows people to understand complex information and use it to make decisions and take actions against issues. This is incredibly important for influential communications, as narratives and visuals are an effective medium that allows our brain to understand information better, remember it, and make informed decisions.

The Bottom Line: Big Decisions Require Big Data Contextualization

When it comes to expansion and taking your business to another level, it’s incredibly important to include big data analysis and contextualization into your efforts. With a better understanding of data – such as what it is and how contextualization can make it actionable, you can create impactful strategies and take the right steps to help your company grow and motivate your employees.

Knowing the basics should help you make better decisions in the future, though working with big data analytics companies like Gemini Data is key for accurate, effective, and strategic data analysis.

If you’re interested in learning more about data contextualization and how it can help your business, reach out to us today. We help organizations construct a connected view of their business and can help solve your biggest data challenges, enabling you to understand and share data stories.